ABSTRACT Project 3 Project III: Understanding Risk for Early Language and Literacy Difficulties in Young Children The proposed research project investigates a broad array of early child, familial, and environmental correlates and predictors of language and early literacy difficulties and disabilities to improve our capacity to identify children at heightened risk for these developmental problems. Although there is significant awareness of the risk to typical language and early literacy development posed by familial history, early language delay, and socio-demographic and environmental influences, there remains a sizable gap in the ability to identify which specific children with one or more known risk factors will achieve normative developmental milestones by school entry versus which children will remain at or accrue high-risk status and be well below typical skill levels at school entry. Earlier identification of high-risk children is crucial to prevent the development of learning disabilities or to minimize their duration and severity. We focus on a group of children at known risk, namely 2- year olds with a familial history of language impairment, reading impairment, or both, to explore how to maximize the sensitivity and specificity of early predictors of learning difficulties at age 5. Once selected from within a large screened sample, we will trace the development and experiences of these 250 children across four years. Specific aims of the project include developing a theoretically-grounded multivariate model to improve early prediction of risk for both language and literacy difficulties, exploring the simultaneous co- developmental trajectories and potential reciprocal influences between language, phonological awareness, memory and early literacy skills, and utilizing meta-analytic methods to identify and quantitatively combine robust predictors of early language and emergent literacy skill development. Innovations of the project include a highly economically and ethnically diverse longitudinal sample initiated when children are just 24 months, complex multivariate prediction models including an extensive set of child, familial, and outside the home environmental influences, advanced statistical methods including machine learning and bivariate latent change models, and inclusion of both off-line standardized and on-line, processing-based measures of early language. A further innovation is our plan to use integrated data analysis to link the developmental trajectory of these children with that of preschool and school age children in three other longitudinal studies. Results of the proposed investigations have the potential to improve early identification of learning disabilities by identifying combinations of key predictors and add to knowledge regarding the interplay of language, cognitive and early literacy skills in a critical early developmental period.